{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 分组计算\n",
    "* Apply\n",
    "* Transform\n",
    "* Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option('display.float_format', '{:,.1f}'.format)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('groupby.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat=pd.read_csv('./data/weather-6m.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>air_temp</th>\n",
       "      <th>dew_point</th>\n",
       "      <th>wind_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-133.0</td>\n",
       "      <td>-167.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-133.0</td>\n",
       "      <td>-161.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-122.0</td>\n",
       "      <td>-156.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>-117.0</td>\n",
       "      <td>-150.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-111.0</td>\n",
       "      <td>-150.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  hour  air_temp  dew_point  wind_speed\n",
       "0  2009      1    1     1    -133.0     -167.0        15.0\n",
       "1  2009      1    1     2    -133.0     -161.0        26.0\n",
       "2  2009      1    1     3    -122.0     -156.0         0.0\n",
       "3  2009      1    1     4    -117.0     -150.0         0.0\n",
       "4  2009      1    1     5    -111.0     -150.0        15.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4340, 7)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Apply\n",
    "* Lambda - 一个group返回一个值\n",
    "* Lambda - 一个group返回多个值\n",
    "* 自定义操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lambda - 一个group返回一个值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "grp=dat.groupby(['month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key:1\n",
      "   year  month  day  hour  air_temp  dew_point  wind_speed\n",
      "0  2009      1    1     1    -133.0     -167.0        15.0\n",
      "1  2009      1    1     2    -133.0     -161.0        26.0\n",
      "2  2009      1    1     3    -122.0     -156.0         0.0\n",
      "3  2009      1    1     4    -117.0     -150.0         0.0\n",
      "4  2009      1    1     5    -111.0     -150.0        15.0\n",
      "5  2009      1    1     6       nan        nan         nan\n",
      "6  2009      1    1     7     -94.0     -144.0        31.0\n",
      "7  2009      1    1     8     -89.0     -139.0        31.0\n",
      "8  2009      1    1     9     -83.0     -139.0        41.0\n",
      "9  2009      1    1    10     -78.0     -139.0        57.0\n",
      "key:2\n",
      "     year  month  day  hour  air_temp  dew_point  wind_speed\n",
      "743  2009      2    1     0      28.0      -17.0        82.0\n",
      "744  2009      2    1     1      28.0      -17.0        82.0\n",
      "745  2009      2    1     2      33.0      -11.0        93.0\n",
      "746  2009      2    1     3      28.0      -11.0        77.0\n",
      "747  2009      2    1     4      28.0      -11.0        67.0\n",
      "748  2009      2    1     5      22.0      -17.0        67.0\n",
      "749  2009      2    1     6       nan        nan         nan\n",
      "750  2009      2    1     7      17.0      -33.0        67.0\n",
      "751  2009      2    1     8      17.0      -39.0        67.0\n",
      "752  2009      2    1     9       0.0      -50.0        62.0\n",
      "key:3\n",
      "      year  month  day  hour  air_temp  dew_point  wind_speed\n",
      "1415  2009      3    1     0     -72.0     -106.0        21.0\n",
      "1416  2009      3    1     1     -72.0     -100.0        15.0\n",
      "1417  2009      3    1     2     -72.0     -100.0        26.0\n",
      "1418  2009      3    1     3     -72.0     -100.0        31.0\n",
      "1419  2009      3    1     4     -72.0     -117.0        31.0\n",
      "1420  2009      3    1     5     -72.0     -117.0        31.0\n",
      "1421  2009      3    1     6       nan        nan         nan\n",
      "1422  2009      3    1     7     -72.0     -133.0        62.0\n",
      "1423  2009      3    1     8     -78.0     -161.0        67.0\n",
      "1424  2009      3    1     9     -83.0     -161.0        46.0\n",
      "key:4\n",
      "      year  month  day  hour  air_temp  dew_point  wind_speed\n",
      "2159  2009      4    1     0      61.0       56.0        41.0\n",
      "2160  2009      4    1     1      72.0       33.0        46.0\n",
      "2161  2009      4    1     2      44.0        6.0        67.0\n",
      "2162  2009      4    1     3      33.0       -6.0        46.0\n",
      "2163  2009      4    1     4      33.0      -11.0        51.0\n",
      "2164  2009      4    1     5      28.0      -22.0        51.0\n",
      "2165  2009      4    1     6       nan        nan         nan\n",
      "2166  2009      4    1     7      33.0      -22.0        62.0\n",
      "2167  2009      4    1     8      33.0      -22.0        57.0\n",
      "2168  2009      4    1     9      28.0      -22.0        57.0\n",
      "key:5\n",
      "      year  month  day  hour  air_temp  dew_point  wind_speed\n",
      "2879  2009      5    1     0     161.0      133.0        26.0\n",
      "2880  2009      5    1     1     150.0      133.0        41.0\n",
      "2881  2009      5    1     2     150.0      133.0        36.0\n",
      "2882  2009      5    1     3     161.0      139.0        62.0\n",
      "2883  2009      5    1     4     156.0      133.0        46.0\n",
      "2884  2009      5    1     5     150.0      133.0        46.0\n",
      "2885  2009      5    1     6       nan        nan         nan\n",
      "2886  2009      5    1     7     139.0       89.0        67.0\n",
      "2887  2009      5    1     8     117.0       72.0        46.0\n",
      "2888  2009      5    1     9     106.0       67.0        46.0\n",
      "key:6\n",
      "      year  month  day  hour  air_temp  dew_point  wind_speed\n",
      "3623  2009      6    1     0     139.0       17.0        41.0\n",
      "3624  2009      6    1     1     133.0       28.0        21.0\n",
      "3625  2009      6    1     2     133.0       22.0        26.0\n",
      "3626  2009      6    1     3     139.0       17.0        41.0\n",
      "3627  2009      6    1     4     144.0       17.0        41.0\n",
      "3628  2009      6    1     5     144.0       28.0        41.0\n",
      "3629  2009      6    1     6       nan        nan         nan\n",
      "3630  2009      6    1     7     111.0       72.0        36.0\n",
      "3631  2009      6    1     8     111.0       83.0         0.0\n",
      "3632  2009      6    1     9     122.0       94.0        36.0\n"
     ]
    }
   ],
   "source": [
    "for key,dat_df in grp:\n",
    "    print('key:'+str(key))\n",
    "    print(dat_df.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wind_speed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       wind_speed\n",
       "month            \n",
       "1             5.5\n",
       "2             5.2\n",
       "3             5.3\n",
       "4             5.4\n",
       "5             7.5\n",
       "6             5.6"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.groupby(['month'])[['wind_speed']].apply(lambda x:x.max()/x.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>air_temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2,544.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-126.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>171.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>352.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>373.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       air_temp\n",
       "month          \n",
       "1      -2,544.8\n",
       "2        -126.7\n",
       "3          65.7\n",
       "4         171.6\n",
       "5         352.2\n",
       "6         373.1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp[['air_temp']].apply(lambda x:x.sum()/x.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-294.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-71,254.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-194.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>-19,007.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-133.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>14,987.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-28.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>48,553.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>99,659.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>33.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>124,248.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         min   max       sum\n",
       "month                       \n",
       "1     -294.0  28.0 -71,254.0\n",
       "2     -194.0 150.0 -19,007.0\n",
       "3     -133.0 228.0  14,987.0\n",
       "4      -28.0 283.0  48,553.0\n",
       "5       11.0 283.0  99,659.0\n",
       "6       33.0 333.0 124,248.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp['air_temp'].agg(['min','max','sum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lambda - 一个group返回多个值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>sum/max</th>\n",
       "      <th>max/std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>258.2</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>236.4</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>213.9</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>255.2</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>184.1</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>181.4</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month  sum/max  max/std\n",
       "0      1    258.2      5.5\n",
       "1      2    236.4      5.2\n",
       "2      3    213.9      5.3\n",
       "3      4    255.2      5.4\n",
       "4      5    184.1      7.5\n",
       "5      6    181.4      5.6"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp['wind_speed'].apply(lambda x:pd.DataFrame([[x.sum()/x.max()],[x.max()/x.std()]],index=['sum/max','max/std'],columns=['column']).T).reset_index().drop(['level_1'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[[SUM/MAX],\n",
    "[MAX/STD]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>wind_speed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
       "      <th>0.1</th>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0.1</th>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
       "      <th>0.1</th>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0.1</th>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>134.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">5</th>\n",
       "      <th>0.1</th>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0.1</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           wind_speed\n",
       "month                \n",
       "1     0.1        15.0\n",
       "      0.5        41.0\n",
       "      1.0       118.0\n",
       "2     0.1        21.0\n",
       "      0.5        46.0\n",
       "      1.0       129.0\n",
       "3     0.1        15.0\n",
       "      0.5        41.0\n",
       "      1.0       149.0\n",
       "4     0.1        21.0\n",
       "      0.5        46.0\n",
       "      1.0       134.0\n",
       "5     0.1        15.0\n",
       "      0.5        41.0\n",
       "      1.0       165.0\n",
       "6     0.1         0.0\n",
       "      0.5        31.0\n",
       "      1.0       124.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp[['wind_speed']].apply(lambda x:x.quantile([0.1,0.5,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>wind_speed</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.5</th>\n",
       "      <th>1.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>134.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "wind_speed  0.1  0.5   1.0\n",
       "month                     \n",
       "1          15.0 41.0 118.0\n",
       "2          21.0 46.0 129.0\n",
       "3          15.0 41.0 149.0\n",
       "4          21.0 46.0 134.0\n",
       "5          15.0 41.0 165.0\n",
       "6           0.0 31.0 124.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp.apply(lambda x:x['wind_speed'].quantile([0.1,0.5,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max/std</th>\n",
       "      <th>sum/max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.5</td>\n",
       "      <td>258.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.2</td>\n",
       "      <td>236.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.3</td>\n",
       "      <td>213.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.4</td>\n",
       "      <td>255.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.5</td>\n",
       "      <td>184.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.6</td>\n",
       "      <td>181.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       max/std  sum/max\n",
       "month                  \n",
       "1          5.5    258.2\n",
       "2          5.2    236.4\n",
       "3          5.3    213.9\n",
       "4          5.4    255.2\n",
       "5          7.5    184.1\n",
       "6          5.6    181.4"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp.apply(lambda x:pd.Series([x['wind_speed'].max()/x['wind_speed'].std(),x['wind_speed'].sum()/x['wind_speed'].max()],index=['max/std','sum/max']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 自定义操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lambda x:xiloc[x['wind_speed'].idxmax()]['hour'],..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fun(data):\n",
    "    idx1=data[['air_temp','wind_speed']].idxmax()\n",
    "    #print(idx1)\n",
    "    idx2=data['dew_point'].idxmin()\n",
    "    l=data.loc[idx1]['hour'].values.tolist()\n",
    "    l.append(data.loc[idx2]['hour'])\n",
    "    return pd.Series(l,index=['air','wind','dew']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>air</th>\n",
       "      <th>wind</th>\n",
       "      <th>dew</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>22.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">6</th>\n",
       "      <th>26</th>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           air  wind  dew\n",
       "month day                \n",
       "1     1   22.0  18.0  1.0\n",
       "      2    0.0  12.0 21.0\n",
       "      3   23.0  21.0  3.0\n",
       "      4    3.0   0.0 23.0\n",
       "      5   20.0   0.0  8.0\n",
       "...        ...   ...  ...\n",
       "6     26  17.0  18.0 23.0\n",
       "      27  17.0   0.0  7.0\n",
       "      28  19.0  19.0 18.0\n",
       "      29   0.0  18.0  0.0\n",
       "      30   0.0   0.0 16.0\n",
       "\n",
       "[181 rows x 3 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.groupby(['month','day']).apply(lambda x:fun(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>air</th>\n",
       "      <th>wind</th>\n",
       "      <th>dew</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>22.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">6</th>\n",
       "      <th>26</th>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           air  wind  dew\n",
       "month day                \n",
       "1     1   22.0  18.0  1.0\n",
       "      2    0.0  12.0 21.0\n",
       "      3   23.0  21.0  3.0\n",
       "      4    3.0   0.0 23.0\n",
       "      5   20.0   0.0  8.0\n",
       "...        ...   ...  ...\n",
       "6     26  17.0  18.0 23.0\n",
       "      27  17.0   0.0  7.0\n",
       "      28  19.0  19.0 18.0\n",
       "      29   0.0  18.0  0.0\n",
       "      30   0.0   0.0 16.0\n",
       "\n",
       "[181 rows x 3 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.groupby(['month','day']).apply(fun)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "1   42.8\n",
       "2   48.0\n",
       "3   44.7\n",
       "4   49.5\n",
       "5   42.6\n",
       "6   32.7\n",
       "Name: wind_speed, dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp['wind_speed'].apply(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>air_temp</th>\n",
       "      <th>dew_point</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_monthly_mean</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-133.0</td>\n",
       "      <td>-167.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>42.8</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-133.0</td>\n",
       "      <td>-161.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>42.8</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-122.0</td>\n",
       "      <td>-156.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>-117.0</td>\n",
       "      <td>-150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-111.0</td>\n",
       "      <td>-150.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>42.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4335</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>19</td>\n",
       "      <td>178.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4336</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>189.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4337</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>183.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4338</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>189.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4339</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>183.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4340 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year  month  day  hour  air_temp  dew_point  wind_speed  \\\n",
       "0     2009      1    1     1    -133.0     -167.0        15.0   \n",
       "1     2009      1    1     2    -133.0     -161.0        26.0   \n",
       "2     2009      1    1     3    -122.0     -156.0         0.0   \n",
       "3     2009      1    1     4    -117.0     -150.0         0.0   \n",
       "4     2009      1    1     5    -111.0     -150.0        15.0   \n",
       "...    ...    ...  ...   ...       ...        ...         ...   \n",
       "4335  2009      6   30    19     178.0      111.0        41.0   \n",
       "4336  2009      6   30    20     189.0      117.0        46.0   \n",
       "4337  2009      6   30    21     183.0      111.0        36.0   \n",
       "4338  2009      6   30    22     189.0      111.0        46.0   \n",
       "4339  2009      6   30    23     183.0      117.0        46.0   \n",
       "\n",
       "      wind_monthly_mean  \n",
       "0                  42.8  \n",
       "1                  42.8  \n",
       "2                  42.8  \n",
       "3                  42.8  \n",
       "4                  42.8  \n",
       "...                 ...  \n",
       "4335               32.7  \n",
       "4336               32.7  \n",
       "4337               32.7  \n",
       "4338               32.7  \n",
       "4339               32.7  \n",
       "\n",
       "[4340 rows x 8 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat['wind_monthly_mean']=grp['wind_speed'].transform(np.mean)\n",
    "dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4340, 7)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>air_temp</th>\n",
       "      <th>dew_point</th>\n",
       "      <th>wind_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-133.0</td>\n",
       "      <td>-167.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-133.0</td>\n",
       "      <td>-161.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-122.0</td>\n",
       "      <td>-156.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>-117.0</td>\n",
       "      <td>-150.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-111.0</td>\n",
       "      <td>-150.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  hour  air_temp  dew_point  wind_speed\n",
       "0  2009      1    1     1    -133.0     -167.0        15.0\n",
       "1  2009      1    1     2    -133.0     -161.0        26.0\n",
       "2  2009      1    1     3    -122.0     -156.0         0.0\n",
       "3  2009      1    1     4    -117.0     -150.0         0.0\n",
       "4  2009      1    1     5    -111.0     -150.0        15.0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      42.8\n",
       "1      42.8\n",
       "2      42.8\n",
       "3      42.8\n",
       "4      42.8\n",
       "       ... \n",
       "4335   32.7\n",
       "4336   32.7\n",
       "4337   32.7\n",
       "4338   32.7\n",
       "4339   32.7\n",
       "Name: month, Length: 4340, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat['month'].map(grp['wind_speed'].apply(np.mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      42.8\n",
       "1      42.8\n",
       "2      42.8\n",
       "3      42.8\n",
       "4      42.8\n",
       "       ... \n",
       "4335   32.7\n",
       "4336   32.7\n",
       "4337   32.7\n",
       "4338   32.7\n",
       "4339   32.7\n",
       "Name: wind_speed, Length: 4340, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp['wind_speed'].transform(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>air_temp</th>\n",
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       "      <th>wind_monthly_mean</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2879</th>\n",
       "      <td>2009</td>\n",
       "      <td>5</td>\n",
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       "      <td>0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>42.6</td>\n",
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       "    <tr>\n",
       "      <th>2880</th>\n",
       "      <td>2009</td>\n",
       "      <td>5</td>\n",
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       "      <td>1</td>\n",
       "      <td>150.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>42.6</td>\n",
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       "    <tr>\n",
       "      <th>2881</th>\n",
       "      <td>2009</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>42.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2882</th>\n",
       "      <td>2009</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>161.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>42.6</td>\n",
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       "    <tr>\n",
       "      <th>2883</th>\n",
       "      <td>2009</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>156.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>42.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4335</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>19</td>\n",
       "      <td>178.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4336</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>189.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4337</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>183.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4338</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>189.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4339</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>183.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1461 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year  month  day  hour  air_temp  dew_point  wind_speed  \\\n",
       "2879  2009      5    1     0     161.0      133.0        26.0   \n",
       "2880  2009      5    1     1     150.0      133.0        41.0   \n",
       "2881  2009      5    1     2     150.0      133.0        36.0   \n",
       "2882  2009      5    1     3     161.0      139.0        62.0   \n",
       "2883  2009      5    1     4     156.0      133.0        46.0   \n",
       "...    ...    ...  ...   ...       ...        ...         ...   \n",
       "4335  2009      6   30    19     178.0      111.0        41.0   \n",
       "4336  2009      6   30    20     189.0      117.0        46.0   \n",
       "4337  2009      6   30    21     183.0      111.0        36.0   \n",
       "4338  2009      6   30    22     189.0      111.0        46.0   \n",
       "4339  2009      6   30    23     183.0      117.0        46.0   \n",
       "\n",
       "      wind_monthly_mean  \n",
       "2879               42.6  \n",
       "2880               42.6  \n",
       "2881               42.6  \n",
       "2882               42.6  \n",
       "2883               42.6  \n",
       "...                 ...  \n",
       "4335               32.7  \n",
       "4336               32.7  \n",
       "4337               32.7  \n",
       "4338               32.7  \n",
       "4339               32.7  \n",
       "\n",
       "[1461 rows x 8 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.groupby(['month']).filter(lambda x:x['air_temp'].min()>=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "1   0.0\n",
       "2   0.0\n",
       "3   0.0\n",
       "4   0.0\n",
       "5   0.0\n",
       "6   0.0\n",
       "Name: wind_speed, dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp['wind_speed'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-22.0</td>\n",
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       "      <td>67.0</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>-67.0</td>\n",
       "      <td>57.0</td>\n",
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       "      <td>106.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>32.7</td>\n",
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       "    <tr>\n",
       "      <th>4288</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>267.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4289</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>21</td>\n",
       "      <td>267.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>32.7</td>\n",
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       "    <tr>\n",
       "      <th>4290</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>22</td>\n",
       "      <td>261.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>32.7</td>\n",
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       "    <tr>\n",
       "      <th>4291</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
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       "      <td>23</td>\n",
       "      <td>256.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>696 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year  month  day  hour  air_temp  dew_point  wind_speed  \\\n",
       "23    2009      1    2     0     -17.0      -67.0        46.0   \n",
       "24    2009      1    2     1     -17.0      -67.0        46.0   \n",
       "25    2009      1    2     2     -17.0      -67.0        67.0   \n",
       "26    2009      1    2     3     -22.0      -67.0        67.0   \n",
       "27    2009      1    2     4     -28.0      -67.0        57.0   \n",
       "...    ...    ...  ...   ...       ...        ...         ...   \n",
       "4287  2009      6   28    19     267.0      106.0       124.0   \n",
       "4288  2009      6   28    20     267.0      106.0        98.0   \n",
       "4289  2009      6   28    21     267.0      106.0        72.0   \n",
       "4290  2009      6   28    22     261.0       94.0        93.0   \n",
       "4291  2009      6   28    23     256.0       94.0        67.0   \n",
       "\n",
       "      wind_monthly_mean  \n",
       "23                 42.8  \n",
       "24                 42.8  \n",
       "25                 42.8  \n",
       "26                 42.8  \n",
       "27                 42.8  \n",
       "...                 ...  \n",
       "4287               32.7  \n",
       "4288               32.7  \n",
       "4289               32.7  \n",
       "4290               32.7  \n",
       "4291               32.7  \n",
       "\n",
       "[696 rows x 8 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.groupby(['month','day']).filter(lambda x:x['wind_speed'].max()>100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4340, 8)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>air_temp</th>\n",
       "      <th>dew_point</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_monthly_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-133.0</td>\n",
       "      <td>-167.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>42.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-133.0</td>\n",
       "      <td>-161.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>42.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-122.0</td>\n",
       "      <td>-156.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>-117.0</td>\n",
       "      <td>-150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-111.0</td>\n",
       "      <td>-150.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>42.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3618</th>\n",
       "      <td>2009</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>161.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>42.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3619</th>\n",
       "      <td>2009</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>161.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>42.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3620</th>\n",
       "      <td>2009</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>156.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>42.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3621</th>\n",
       "      <td>2009</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>150.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>42.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3622</th>\n",
       "      <td>2009</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>144.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>42.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2231 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year  month  day  hour  air_temp  dew_point  wind_speed  \\\n",
       "0     2009      1    1     1    -133.0     -167.0        15.0   \n",
       "1     2009      1    1     2    -133.0     -161.0        26.0   \n",
       "2     2009      1    1     3    -122.0     -156.0         0.0   \n",
       "3     2009      1    1     4    -117.0     -150.0         0.0   \n",
       "4     2009      1    1     5    -111.0     -150.0        15.0   \n",
       "...    ...    ...  ...   ...       ...        ...         ...   \n",
       "3618  2009      5   31    19     161.0       83.0        51.0   \n",
       "3619  2009      5   31    20     161.0       72.0        57.0   \n",
       "3620  2009      5   31    21     156.0       33.0        57.0   \n",
       "3621  2009      5   31    22     150.0       17.0        57.0   \n",
       "3622  2009      5   31    23     144.0       11.0        57.0   \n",
       "\n",
       "      wind_monthly_mean  \n",
       "0                  42.8  \n",
       "1                  42.8  \n",
       "2                  42.8  \n",
       "3                  42.8  \n",
       "4                  42.8  \n",
       "...                 ...  \n",
       "3618               42.6  \n",
       "3619               42.6  \n",
       "3620               42.6  \n",
       "3621               42.6  \n",
       "3622               42.6  \n",
       "\n",
       "[2231 rows x 8 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.groupby(['month']).filter(lambda x:31 in x['day'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5], dtype=int64)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.groupby(['month']).filter(lambda x:31 in x['day'].unique())['month'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 课后练习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = pd.read_csv('./data/weather-6m.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算每个air_temp 基于每月平均值的zscore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随机选取50个数据，运用随机产生的数据，对每个月计算，数据量在每天的分布， 比如 10%的数据在第一天采集到"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在dat数据基础上，重新生成一个代表每天air_temp平均值的column,命名为daily_air_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在dat中去掉每日最大air_temp 小于10的日子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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